A critical element in the operation of air-borne and space-borne imaging systems is sensor calibration on the ground before launch or flight. However, it is entirely possible that physical conditions within the imaging system may change from the calibration laboratory setting in such a way so as to skew the calibration values. The calibration performance thus becomes suspect until validation after deployment. Indeed, to assure the most accurate operation, absolute calibration is necessary.
This is particularly true for geometric calibration of an imaging system which is required for making accurate geometric measurements from image data. Geometric calibration of an imaging system is performed by recording visible control points whose geometric properties are known and fitting a camera model to the positions of the control points in the calibration images. The camera model contains multiple parameters (also known as calibration coefficients) that must be derived from the calibration data in order to transform the recorded image into a geometric distortion-free image. There are two main kinds of parameters. First are the external parameters that account for orientation, position and motion of the imaging platform relative to the coordinate frame of the control points. For systems on-orbit, external parameters must also account for the curvature of the earth. Second, are the internal parameters that determine the projection of the control points from the imaging systems optical coordinate frame on to the coordinate frame of the image plane containing the radiation detection elements for generating the image pixels. This may include stitching together image components from multiple detector chips or multiple image planes. Once, the parameters are derived, the control points reveal systematic errors in the camera model, providing figures of merit for determining and trending calibration performance.
Previous art for operational control points rely on the intersection of roads, parking lot lines, sidewalks, roof corners on buildings or any high contrast geometric feature that can be associated with a ground position referenced to an absolute geodetic coordinate system and in turn provide a visible location in an image as a geometric calibration control point. However, such extended axial features have limited accuracy in pinpointing their location in a pixilated image. In laboratory testing, circular control points have proven to be optimal in geometric calibration since the centriod of the projected circles can be detected with high subpixel accuracy. However, once deployed, high contrast and well defined circular control points become unavailable and geometric accuracy knowledge becomes less attainable.
Thus, a need exists in the art for improved systems and methods for geometric calibration of remote sensors.